STYLE: Rename the EddyMotionGPR instance to honor better its classname

Rename the `EddyMotionGPR` instance to honor better its classname.

scripts/dwi_gp_estimation_error_analysis.py

@@ -47,7 +47,7 @@ def cross_validate(
     X: np.ndarray,
     y: np.ndarray,
     cv: int,
-    gpm: EddyMotionGPR,
+    gpr: EddyMotionGPR,
 ) -> dict[int, list[tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]]]:
     """
     Perform the experiment by estimating the dMRI signal using a Gaussian process model.
@@ -60,7 +60,7 @@ def cross_validate(
         DWI signal.
     cv : :obj:`int`
         number of folds
-    gpm : obj:`~eddymotion.model._sklearn.EddyMotionGPR`
+    gpr : obj:`~eddymotion.model._sklearn.EddyMotionGPR`
         The eddymotion Gaussian process regressor object.
 
     Returns
@@ -71,7 +71,7 @@ def cross_validate(
     """
 
     rkf = RepeatedKFold(n_splits=cv, n_repeats=120 // cv)
-    scores = cross_val_score(gpm, X, y, scoring="neg_root_mean_squared_error", cv=rkf)
+    scores = cross_val_score(gpr, X, y, scoring="neg_root_mean_squared_error", cv=rkf)
     return scores
 
 
@@ -176,7 +176,7 @@ def main() -> None:
     a = 1.15
     lambda_s = 120
     alpha = 100
-    gpm = EddyMotionGPR(
+    gpr = EddyMotionGPR(
         kernel=SphericalKriging(a=a, lambda_s=lambda_s),
         alpha=alpha,
         optimizer=None,
@@ -186,7 +186,7 @@ def main() -> None:
     scores = defaultdict(list, {})
     for n in args.kfold:
         for i in range(args.repeats):
-            cv_scores = -1.0 * cross_validate(X, y.T, n, gpm)
+            cv_scores = -1.0 * cross_validate(X, y.T, n, gpr)
             scores["rmse"] += cv_scores.tolist()
             scores["repeat"] += [i] * len(cv_scores)
             scores["n_folds"] += [n] * len(cv_scores)
@@ -202,7 +202,7 @@ def main() -> None:
     print(grouped[["rmse"]].std())
 
     cv = KFold(n_splits=3, shuffle=False, random_state=None)
-    predictions = cross_val_predict(gpm, X, y.T, cv=cv)
+    predictions = cross_val_predict(gpr, X, y.T, cv=cv)
     testsims.serialize_dwi(predictions.T, args.dwi_pred_data_fname)